A chart or graph is described in Wikipedia as a type of information graphic or graphic organizer that represents tabular numeric data and/or functions. Charts are often used to make it easier to understand large quantities of data and the relationship between different parts of the data. Charts can usually be read more quickly than the raw data that they come from. They are used in a wide variety of fields, and can be created by hand (often on graph paper) or by computer using a charting application.
Traditional charts use well established and often poorly implemented ways of representing data. Many tools exist to help the user construct very sophisticated representations of data but that sophistication typically results in less meaningful charts. Embodiments of the present invention aim to overcome this problem.
It is known to use charting wizards such as those that are available in Excel and various other systems such as those provided by, for example, IBM. In addition there are multiple Business Intelligence (BI) tools available to users to enable users to analyze data in an attempt to create meaningful feedback. However, as the amount of data increases, so does the complexity of the visual representations created by the analysis of the data. These complex representations can end up swamping parts of the visual representation that is most required and relevant to an end user.
In addition, known systems provide a standardized list of options to all users which the user then must wade through and try and determine which of the options available are most suitable for representing their particular data. This can result in the user mismatching the data being represented with the chosen visual representation so that the resultant representation does not clearly, accurately and succinctly identify any issues with, or convey information about, the data. This can result in the user missing particularly important features of the data due to those features not being represented in the most appropriate manner.
Also, although there are many sophisticated visualization algorithms that do exist and are being developed for specific functions, these algorithms are not provided to a user in a manner that guides the user to easily pick the data to be represented, pick the correct summaries of the data, pick the right dimensions to be represented, pick the right forms of visual representation, or choose unique visual designs to create a collection of visualizations that help someone run their business.
Further, the focus of existing known methods is on providing a single visual design, or type of visual or graphical representation, to represent data. That is, to produce, for example, a single bar graph to be displayed, or a single pie chart to be printed. This is very limiting to a user who may want to show various different aspects of the data in a single document.
Business measures are a well known means of identifying a manageable number of algorithms for which to run a business. However, these business measures merely represent a single dimension of the data, or even only a single number, and so are particularly limiting in respect of the data that they represent. Further, the business measures merely represent data and do not include any further functional capabilities.
This is particularly pertinent to the Gaming Industry, because gaming venues can collect data, which can be in large volumes, or diverse, detailed, timely or accurate information, on their customers' purchasing behavior or movements within the facility in the normal course of providing the gaming business or from external sources. Examples of this data include the amount gambled by game, how much time has been spent playing each game, what has occurred (e.g., winning of jackpots) during customers' game play. Additionally, similar data is collected regarding non-gaming purchases (e.g., food and beverage, special events, lodging). Finally, customers may be issued credit so data associated with granting credit lines (e.g., credit rating, credit limits, etc.) is also collected. This potentially large or dispersed data collection may be further refined by collecting into a centrally accessible point. This centrally accessible capability can be implanted in a number of ways including, a data warehouse or a data mart or a federated information collection.
The often related or diverse and sometimes large volumes of data collected by the Gaming Industry on a variety of areas of the business, including data on their customers, their operations or external data sets, benefit from methods for understanding this data. These methods may range from the simple analytical views to sophisticated analytical methods as herein described.
R-tree indexing methodologies, as well as other indexing methodologies, are used in conjunction with databases to categorize data and place the data in a hierarchical format. It is known to use self organizing maps to visually represent data. However, self organizing maps can be very difficult and arduous to interpret. Also, it has not previously been known to use the indexing methodologies, in particular the R-tree indexing, as a display mechanism on its own.
Classification algorithms, such as fast clustering genetic algorithms or dimension reduction algorithms, can result in highly complicated structures. These may include 2 displays, the R-Tree, which may provide interactive insight into, for instance, the relationship between a customer's play, the types of games played, and the location of the game relative to other games.
Various other references to the prior art and its associated problems are made throughout the following description.
In current gaming systems it is possible to determine the “actual win” associated with a gaming asset or device, such as a single or group of slot gaming machines or a single or group of gaming tables (e.g. electronic gaming tables), by determining the amount of money generated by the gaming assets.
The “actual win” generated by a gaming asset is the amount of money received from players (or customers) when the player loses money when using a gaming asset, e.g. loses a bet. For example, a Roulette player who places a bet of $50 on RED on a Roulette table will lose $50 if the outcome is BLACK. Therefore, the casino actual win for that transaction is $50.
The “theoretical win” value is based on the calculated probability of a gaming asset winning (hold percentage). That is, for example, if the probability of the casino profiting on transactions placed on a Roulette table is at 5%, then the theoretical win for the $50 transaction discussed above is 5% of $50, i.e. $2.50. The remaining money $47.50 is expected to be returned to players over a period of time.
However, current systems, including monitoring tools and customer relationship tools, do not take into account various other forms of gaming accounting, such as the use of bonuses, jackpots and free-plays for example, which can affect the amount of money won by customers. This can create distortion against the actual money generated in measurements associated with these calculations.
For example, these forms of accounting can significantly alter the amount of profit or loss associated with a gaming asset.
It is known to use neural networks in an attempt to predict how gaming assets will perform based on various inputs, such as the revenue received, spatial position and time of day. One example is discussed in U.S. Pat. No. 6,871,194 Interaction Prediction System and Method. The neural network uses a back propagation methodology in order learn how to predict future financial transactions with the gaming assets. However, these systems generally try to predict win values purely using self teaching loops based on previous performance and do not use additional inputs to aid in the calculation.
The present invention aims to overcome, or at least alleviate, some or all of the mentioned problems, or to at least provide the public with a useful choice.